Data Analytics Plus Data Science

Data Analytics + Data Science

Excel, Power BI, Tableau, SQL, Python, Machine Learning, Project

Data Analytics + Data Science

Excel, Power BI, Tableau, SQL, Python, Machine Learning, Project

In today's data-driven world, organizations are unlocking invaluable insights from their data reservoirs through the synergistic duo of Data Analytics and Data Science. Data Analytics involves extracting, cleaning, and transforming data to uncover patterns and trends, empowering informed decision-making. On the other hand, Data Science delves deeper, employing advanced statistical and machine learning techniques to predict future outcomes and drive innovation. Together, they revolutionize industries, from finance to healthcare, by optimizing processes, enhancing customer experiences, and fueling business growth. Harnessing the power of data, organizations can navigate complexities, seize opportunities, and chart a course towards unparalleled success.

Module 1: Introduction to Data Analytics and Data Science

- Overview of data analytics and data science
- Importance of data-driven decision-making
- Introduction to Excel, Power BI, Tableau, SQL, and Python
- Understanding the role of machine learning in data science
- Overview of project-based learning approach

Module 2: Excel for Data Analysis

- Introduction to Excel for Data Analysis
- Understanding the role of Excel in data analysis
- Introduction to basic Excel functions and formulas relevant to data analysis

Basic Data Analysis Techniques
- Sorting and filtering data
- Using Excel tables for data management
- Basic statistical functions (mean, median, mode, standard deviation, etc.)

Advanced Data Analysis Techniques
- Introduction to PivotTables for summarizing data
- Advanced PivotTable features (slicers, calculated fields, grouping, etc.)
- Performing what-if analysis with scenarios and goal seek

Data Import and Preparation
- Importing data into Excel from various sources (CSV, text files, databases, etc.)
- Cleaning and formatting data for analysis

Data Visualization
- Creating charts and graphs to visualize data
- Customizing chart elements for clarity and aesthetics
- Exploring Excel's interactive data visualization tools
- Incorporating data visualization best practices

Introduction to Power Query
- Overview of Power Query and its benefits
- Importing and transforming data using Power Query
- Combining data from multiple sources
- Cleaning and shaping data with Power Query

Introduction to Power Pivot
- Understanding Power Pivot and its advantages over regular PivotTables
- Importing data into Power Pivot
- Creating relationships between tables
- Building powerful data models for analysis

Module 3: Power BI for Data Analysis

Introduction to Power BI
- Overview of Power BI
- Understanding the Power BI ecosystem
- Installation and setup
- Getting familiar with Power BI Desktop and Power BI Service

Data Transformation and Cleansing
- Cleaning and transforming data using Power Query Editor
- Handling missing or inconsistent data
- Merging and appending queries
- Advanced data transformation techniques

Connecting to Data Sources
- Importing data from various sources (Excel, CSV, databases, web sources, etc.)
- Understanding data shaping and transformation
- Data modeling best practices

Data Modeling
- Introduction to data modeling concepts
- Creating relationships between tables
- Using DAX (Data Analysis Expressions) for calculations and measures
- Introduction to calculated columns and measures

Visualization Techniques
- Creating different types of visualizations (bar charts, line charts, pie charts, etc.)
- Customizing visualizations for better insights
- Using slicers, filters, and drill-downs for interactivity
- Best practices for effective visualizations

Advanced Analytics with Power BI
- Introduction to advanced analytics features (forecasting, clustering, etc.)
- Implementing time intelligence functions in DAX

Power BI Service and Sharing
- Publishing reports and dashboards to Power BI Service
- Configuring dashboards and sharing options
- Collaboration and collaboration features
- Managing security and permissions

Module 4: Tableau Essentials

Introduction to Tableau
- Overview of Tableau and its uses in data analysis.
- Understanding the Tableau interface.
- Connecting to data sources.

Data Preparation
- Data sourcing and importing.

Visualization Basics
- Creating basic visualizations (bar charts, line charts, pie charts, etc.).
- Applying filters and sorting data.
- Formatting visualizations for clarity.

Advanced Visualization Techniques
- Creating interactive dashboards.
- Using parameters and calculated fields.
- Implementing advanced chart types (treemaps, heatmaps, etc.).

Data Analysis
- Exploring trends and patterns in data.
- Conducting ad-hoc analysis with Tableau.
- Utilizing forecasting and trend analysis tools.

Mapping Data
- Geospatial analysis with maps.
. - Analyzing location-based data.

Sharing and Collaboration
- Publishing workbooks to Tableau Public.
- Sharing insights and visualizations with stakeholders.
- Collaborating on projects within Tableau.

Module 5: SQL Fundamentals

Introduction to SQL
- Overview of SQL and its importance in data analysis
- Understanding relational databases
- Introduction to SQL Server Management Studio (SSMS)
- Basic SQL syntax and commands (SELECT, FROM, WHERE, ORDER BY, etc.)

Retrieving Data with SQL
- Retrieving data from a single table using SELECT statement
- Filtering data using WHERE clause
- Sorting data using ORDER BY clause
- Limiting rows using TOP and OFFSET-FETCH clauses

Working with Multiple Tables
- Understanding table joins (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL JOIN)
- Joining tables with WHERE clause
- Joining tables with ON clause
- Combining data from multiple tables using UNION and UNION ALL

Aggregating Data
- Using aggregate functions (COUNT, SUM, AVG, MIN, MAX)
- Grouping data using GROUP BY clause
- Filtering grouped data using HAVING clause
- Understanding rollup and cube operations for subtotal and total calculations

Subqueries and Derived Tables
- Understanding subqueries and their types (scalar, table, correlated)
- Writing subqueries in SELECT, FROM, WHERE clauses
- Working with derived tables (also known as inline views or subquery in FROM clause)

Modifying Data
- Inserting data into tables using INSERT statement
- Updating existing data using UPDATE statement
- Deleting data using DELETE statement
- Transaction management and data integrity (BEGIN TRANSACTION, COMMIT, ROLLBACK)

Advanced SQL Concepts
- Understanding window functions (ROW_NUMBER, RANK, DENSE_RANK, NTILE)
- Using common table expressions (CTEs) for complex queries
- Introduction to stored procedures and functions
- Error handling in SQL

Performance Optimization and Indexing
- Understanding query execution plans
- Identifying performance bottlenecks
- Using indexes for query optimization
- Best practices for writing efficient SQL queries

Module 6: Python for Data Analysis

Introduction to Python
- Introduction to Python programming language
- Installing Python and setting up the development environment
- Basic syntax, data types, and variables
- Control flow statements: if, elif, else, loops
- Functions and modules

Introduction to Data Analysis with Python
- Overview of data analysis and its importance
- Introduction to Python libraries for data analysis: Pandas, NumPy, Matplotlib
- Loading and inspecting data using Pandas
- Basic data manipulation and cleaning techniques

Data Manipulation with Pandas
- Understanding Pandas data structures: Series, DataFrame
- Indexing and selecting data
- Handling missing data
- Data aggregation and grouping
- Merging and joining datasets

Exploratory Data Analysis (EDA)
- Understanding the importance of EDA
- Descriptive statistics: mean, median, mode, standard deviation, etc.
- Data visualization techniques using Matplotlib and Seaborn
- Exploring relationships between variables

Module 7: Machine Learning Basics

- Introduction to machine learning concepts and algorithms
- Supervised learning techniques: regression and classification
- Unsupervised learning techniques: clustering and dimensionality reduction
- Model evaluation and validation techniques in machine learning

Module 8: Advanced Machine Learning

- Introduction to advanced machine learning techniques
- Ensemble methods: bagging, boosting, and stacking
- Introduction to deep learning and neural networks
- Model tuning and optimization techniques in machine learning

Module 8: Data Science Project

- Application of data analytics and data science techniques to real-world projects
- Project planning, scoping, and requirements gathering
- Data exploration, analysis, and visualization
- Building predictive models and interpreting results
- Presentation and communication of project findings

○ Diverse Skill Set: Our course equips you with a diverse skill set spanning both foundational and advanced tools in data analytics and data science. From Excel for basic data manipulation to Python for advanced machine learning, you'll gain proficiency in a range of essential technologies.

○ Industry-Relevant Curriculum: Our curriculum is carefully crafted to align with industry standards and requirements. Whether you're a beginner or an experienced professional, our course offers practical, hands-on training that prepares you for real-world challenges in the field of data analytics and data science.

○ Hands-On Experience: We believe in learning by doing. Throughout the course, you'll have the opportunity to work on real-world projects that simulate scenarios encountered in professional settings. This hands-on experience not only enhances your skill development but also builds your confidence in applying theoretical concepts to practical situations.

○ Expert Guidance: Our instructors are seasoned professionals with extensive experience in the field of data analytics and data science. They provide expert guidance, personalized support, and valuable insights to help you navigate complex concepts and overcome challenges effectively.

○ Career Advancement: Whether you're looking to enter the field of data analytics or seeking to advance your career, our course provides you with the knowledge and skills needed to excel in today's competitive job market. From data analysts to data scientists, our training opens doors to a wide range of exciting career opportunities.

○ Continuous Support: Learning doesn't end when the course concludes. We provide ongoing support and resources to help you stay updated with the latest trends, technologies, and best practices in data analytics and data science. Our goal is to empower you with the skills and knowledge needed for long-term success in your career.

○ Educational Background: While a specific degree is not mandatory, candidates should have a strong foundation in mathematics, statistics, or a related field. A bachelor's degree in computer science, engineering, mathematics, statistics, economics, or a related discipline is preferred but not required.

○ Basic Computer Skills: Candidates should possess basic computer skills and familiarity with using software applications. Proficiency in Microsoft Excel is recommended, as it forms the basis for data manipulation and analysis in the course.

○ Technical Requirements: Candidates must have access to a computer with internet connectivity to participate in online lectures, access course materials, and complete assignments.

○ Enhanced Career Opportunities: Acquiring skills in data analytics and data science opens up a wide range of career opportunities in various industries. From data analysts and business intelligence analysts to data scientists and machine learning engineers, there is a growing demand for professionals with expertise in these fields.

○ Competitive Advantage: In today's competitive job market, possessing in-demand skills such as data analytics, data visualization, programming, and machine learning gives you a competitive edge over other candidates. Employers value individuals who can effectively analyze data to drive informed decision-making and business outcomes.

○ Increased Earning Potential: Professionals with proficiency in data analytics and data science typically command higher salaries compared to those without such skills. By investing in your education and acquiring expertise in these areas, you can potentially increase your earning potential and advance your career trajectory.

○ Versatility and Flexibility: Data analytics and data science skills are highly versatile and can be applied across various industries and job roles. Whether you're interested in finance, healthcare, marketing, or any other field, the ability to analyze data and derive actionable insights is invaluable.

○ Real-World Application: Our course emphasizes practical, hands-on learning, allowing you to apply theoretical concepts to real-world scenarios. Through projects and case studies, you'll gain practical experience in data analysis, visualization, and machine learning, preparing you for the challenges of the workplace.

The scope of learning Data Analytics Plus Data Science, which includes Excel, Power BI, Tableau, SQL, Python, Machine Learning, and Project work, is vast and continually expanding. Here's an overview of the scope and opportunities associated with mastering these skills:

Diverse Career Opportunities: Proficiency in data analytics and data science opens doors to a wide range of career opportunities across various industries. You can pursue roles such as data analyst, business intelligence analyst, data scientist, machine learning engineer, data engineer, and more.

Industry Demand: With the increasing digitization of businesses and the exponential growth of data, the demand for skilled professionals in data analytics and data science is at an all-time high. Organizations across industries are actively seeking individuals who can leverage data to drive insights, innovation, and competitive advantage.

Versatility Across Industries: Data analytics and data science skills are applicable across virtually every industry, including finance, healthcare, retail, e-commerce, technology, manufacturing, marketing, and more. Whether you're interested in finance, healthcare, retail, e-commerce, technology, manufacturing, marketing, or any other field, the ability to analyze data and derive actionable insights is invaluable.

Global Opportunities: Data analytics and data science skills are in demand worldwide. Whether you're based in Karachi, Pakistan, or any other part of the world, mastering these skills opens up global career opportunities, allowing you to work with multinational corporations, tech startups, consulting firms, and more.

Continuous Innovation and Advancement: The field of data analytics and data science is constantly evolving, with new technologies, tools, and techniques emerging regularly. By staying updated with the latest trends and advancements, you can remain competitive in the job market and continue to advance your career.

Entrepreneurial Opportunities: Data analytics and data science skills also provide opportunities for entrepreneurship. You can start your own consulting firm, develop data-driven products or services, or launch a tech startup focused on leveraging data for innovation and impact.

Contribution to Society: Beyond career advancement and entrepreneurship, mastering data analytics and data science skills allows you to make a meaningful contribution to society. Whether it's using data to drive social impact initiatives, address global challenges, or improve public services, your skills can be a force for positive change.

What background knowledge do I need to enroll in the course?

While a specific background is not required, familiarity with basic mathematics, statistics, and computer skills is beneficial. A bachelor's degree in a related field such as computer science, engineering, mathematics, or statistics is preferred but not mandatory.

Do I need prior programming experience to learn Python and other programming languages?

No, prior programming experience is not required. The course covers programming fundamentals, making it suitable for beginners. However, having some exposure to programming concepts can be advantageous.

How will this course benefit my career?

The course equips you with in-demand skills in data analytics, data visualization, programming, and machine learning, making you highly sought after by employers across various industries. It opens up a wide range of career opportunities and enhances your earning potential.

Is the course suitable for beginners or experienced professionals?

The course is designed to accommodate learners of all levels, from beginners to experienced professionals. Whether you're just starting your career in data analytics or looking to advance your skills, the course offers tailored learning experiences to meet your needs.

What is the duration of the course?

The duration and format of the training is 4 months.

Are there any prerequisites for specific modules or topics?

While there are no strict prerequisites for individual modules or topics, some concepts may build upon each other. It's recommended to complete the course sequentially to ensure a thorough understanding of the material. Additionally, completing assignments and projects may require access to specific software tools or platforms.

Is certification available upon completion of the training?

Successful participants will get the AIN GenX and Skill Development Council Karachi certificates.

Is there ongoing support or resources available after completing the training?

Data Analytics + Data Science

Excel, Power BI, Tableau, SQL, Python, Machine Learning, Project

Data Analytics + Data Science

Excel, Power BI, Tableau, SQL, Python, Machine Learning, Project

- Overview of data analytics and data science
- Importance of data-driven decision-making
- Introduction to Excel, Power BI, Tableau, SQL, and Python
- Understanding the role of machine learning in data science
- Overview of project-based learning approach

- Introduction to machine learning concepts and algorithms
- Supervised learning techniques: regression and classification
- Unsupervised learning techniques: clustering and dimensionality reduction
- Model evaluation and validation techniques in machine learning

- Introduction to advanced machine learning techniques
- Ensemble methods: bagging, boosting, and stacking
- Introduction to deep learning and neural networks
- Model tuning and optimization techniques in machine learning

- Application of data analytics and data science techniques to real-world projects
- Project planning, scoping, and requirements gathering
- Data exploration, analysis, and visualization
- Building predictive models and interpreting results
- Presentation and communication of project findings

While a specific background is not required, familiarity with basic mathematics, statistics, and computer skills is beneficial. A bachelor's degree in a related field such as computer science, engineering, mathematics, or statistics is preferred but not mandatory.

No, prior programming experience is not required. The course covers programming fundamentals, making it suitable for beginners. However, having some exposure to programming concepts can be advantageous.

The course equips you with in-demand skills in data analytics, data visualization, programming, and machine learning, making you highly sought after by employers across various industries. It opens up a wide range of career opportunities and enhances your earning potential.

The course is designed to accommodate learners of all levels, from beginners to experienced professionals. Whether you're just starting your career in data analytics or looking to advance your skills, the course offers tailored learning experiences to meet your needs.

The duration and format of the training is 4 months.

Successful participants will get the AIN GenX and Skill Development Council Karachi certificates.

Yes, participants will get continued support through Email and WhatsApp groups.

Instructors

Irfan Bakaly

Noor Surani

Dr. Iftikhar

Who this course is for: